ReviewAnnals of translational medicine2021
Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.
Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 32 citations in OpenAlex.
- Evolving SPECT-CT technology.The British journal of radiology · 2026Review
- Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.Annals of nuclear medicine · 2026Article
- Investigation of Attenuation Correction Methods for Dual-Gated Single Photon Emission Computed Tomography (DG-SPECT).Bioengineering (Basel, Switzerland) · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Technological Advances in SPECT and SPECT/CT Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Verification of image quality improvement of low-count bone scintigraphy using deep learning.Radiological physics and technology · 2024Article
- Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.Diagnostics (Basel, Switzerland) · 2024Review
- Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review.Nutrients · 2023Review
- The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update.Cancers · 2023Review
- Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study.Annals of translational medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Artificial intelligence (AI) has been widely applied to medical imaging. The use of AI for emission computed tomography, particularly single-photon emission computed tomography (SPECT) emerged nearly 30 years ago but has been accelerated in recent years due to the development of AI technology. In this review, we will describe and discuss the progress of AI technology in SPECT imaging. The applications of AI are dispersed in disease prediction and diagnosis, post-reconstruction image denoising, attenuation map generation, and image reconstruction. These applications are relevant to many disease categories such as the neurological disorders, kidney failure, cancer, heart disease, etc. This review summarizes these applications so that SPECT researchers can have a reference overview of the role of AI in current SPECT studies. For each application, we followed the timeline to present the evolution of AI's usage and offered insights on how AI was combined with the knowledge of underlying physics as well as traditional non-learning techniques. Ultimately, AI applications are critical to the progress of modern SPECT technology because they provide compensations for many deficiencies in conventional SPECT imaging methods and demonstrate unparalleled success. Nonetheless, AI also has its own challenges and limitations in the medical field, including SPECT imaging. These fundamental questions are discussed, and possible future directions and countermeasures are suggested.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.